{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifiers and Class Labels\n",
    "\n",
    "This notebook explores how the classification score visualizers handle constraints when it comes to different types of models, data, class labeling schemes, and other parameters. In particular we explore the following:\n",
    "\n",
    "Target Types:\n",
    "\n",
    "- binary\n",
    "- multiclass (3 classes)\n",
    "\n",
    "Target Encoding:\n",
    "\n",
    "- integers\n",
    "- labels \n",
    "\n",
    "Labeling:\n",
    "\n",
    "- list of classes\n",
    "- LabelEncoder\n",
    "- dict encoding\n",
    "- list of more classes than values in y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure we're importing the development version of Yellowbrick\n",
    "import sys\n",
    "sys.path.append(\"../..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use inline so that we can run the notebook multiple times\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import all of the Yellowbrick classifiers\n",
    "from yellowbrick.classifier import *\n",
    "from yellowbrick.exceptions import YellowbrickError\n",
    "from yellowbrick.datasets import load_game, load_occupancy\n",
    "\n",
    "# Import scikit-learn utilities\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB, GaussianNB\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "from collections import namedtuple\n",
    "from functools import partial\n",
    "\n",
    "Dataset = namedtuple(\"Dataset\", \"X,y,classes,encoder\")\n",
    "Split = namedtuple(\"Split\", \"train,test\")\n",
    "\n",
    "\n",
    "make_binary = partial(make_classification,\n",
    "        n_samples=500,\n",
    "        n_features=20,\n",
    "        n_informative=8,\n",
    "        n_redundant=2,\n",
    "        n_classes=2,\n",
    "        n_clusters_per_class=3,\n",
    "    )\n",
    "\n",
    "make_multiclass = partial(make_classification, \n",
    "        n_samples=500,\n",
    "        n_features=20,\n",
    "        n_informative=8,\n",
    "        n_redundant=2,\n",
    "        n_classes=6,\n",
    "        n_clusters_per_class=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the parameters to run against all models \n",
    "# Then restart kernel and run all\n",
    "MODEL = LogisticRegression(solver='lbfgs', multi_class='auto')\n",
    "IS_FITTED = False\n",
    "DATASET = \"multiclass\" \n",
    "TARGET = \"integers\"\n",
    "ENCODER = \"labelencoder\"\n",
    "USE_PANDAS = False\n",
    "\n",
    "\n",
    "def make_dataset(name=DATASET, target=TARGET, encoder=ENCODER, use_pandas=USE_PANDAS):\n",
    "    loader = {\n",
    "        'game': load_game, \n",
    "        'occupancy': load_occupancy,\n",
    "        'binary': make_binary,\n",
    "        'multiclass': make_multiclass,\n",
    "    }.get(name)\n",
    "    \n",
    "    if name in {'game', 'occupancy'}:\n",
    "        dataset = loader(return_dataset=True)\n",
    "        labels = sorted(dataset.meta['labels'].items(), key=lambda i: i[1])\n",
    "\n",
    "        if use_pandas:\n",
    "            X, y = dataset.to_pandas()\n",
    "        else:\n",
    "            X, y = dataset.to_numpy()\n",
    "    else:\n",
    "        X, y = loader()\n",
    "        labels = zip(list('abcdefghijk'), np.unique(y))\n",
    "        \n",
    "    if name == 'game':\n",
    "        X = OneHotEncoder().fit_transform(X)\n",
    "    \n",
    "    # game target is string encoded, occupancy is integer encoded\n",
    "    if target == \"integers\":\n",
    "        if y.dtype.kind != 'i':\n",
    "            y = LabelEncoder().fit_transform(y)\n",
    "    elif target == \"labels\":\n",
    "        if y.dtype.kind == 'i':\n",
    "            rv = {i[1]: i[0] for i in labels.items()}\n",
    "            y = np.array([rv[yi] for yi in y])\n",
    "    else:\n",
    "        raise ValueError(f\"unknown target type '{target}', use integers or labels\")\n",
    "    \n",
    "    c, le = None, None\n",
    "    X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True, stratify=y)\n",
    "    \n",
    "    if encoder == 'list':\n",
    "        c = [l[0] for l in labels]\n",
    "    elif encoder == 'labelencoder':\n",
    "        le = LabelEncoder().fit([l[0] for l in labels])\n",
    "    elif encoder == 'dict':\n",
    "        le = {l[1]: l[0] for l in labels}\n",
    "    elif encoder is None:\n",
    "        c, le = None, None\n",
    "    else:\n",
    "        raise ValueError(f\"unknown encoder type '{encoder}', see make_dataset for choices\")\n",
    "    \n",
    "    return Dataset(Split(X_train, X_test), Split(y_train, y_test), c, le)\n",
    "    \n",
    "\n",
    "def visualize(visualizer, model=MODEL, is_fitted=IS_FITTED, score=True):\n",
    "    if is_fitted:\n",
    "        # This includes both auto and True; fit the model manually if you want the exception raised\n",
    "        model = model.fit(dataset.X.train, dataset.y.train)\n",
    "    _, ax = plt.subplots(figsize=(9,6)) \n",
    "    \n",
    "    try:\n",
    "        oz = visualizer(model, ax=ax, classes=dataset.classes, encoder=dataset.encoder, is_fitted=is_fitted)\n",
    "        oz.fit(dataset.X.train, dataset.y.train)\n",
    "\n",
    "        if score:\n",
    "            oz.score(dataset.X.test, dataset.y.test)\n",
    "\n",
    "        oz.finalize()\n",
    "    except YellowbrickError as e:\n",
    "        print(e)\n",
    "    except Exception as e:\n",
    "        print(\"A NON YB ERROR OCCURRED:\")\n",
    "        print(e)\n",
    "    return oz\n",
    "    \n",
    "    \n",
    "                         \n",
    "dataset = make_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oz = visualize(ClassPredictionError)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oz.score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ClassificationReport(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x12b9f77b8>,\n",
       "                     classes=None,\n",
       "                     cmap=<matplotlib.colors.ListedColormap object at 0x10ff4ae80>,\n",
       "                     encoder=LabelEncoder(), force_model=False, is_fitted=False,\n",
       "                     model=None, support=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(ClassificationReport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConfusionMatrix(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x12db15470>,\n",
       "                classes=None,\n",
       "                cmap=<matplotlib.colors.ListedColormap object at 0x12dc3fa58>,\n",
       "                encoder=LabelEncoder(), fontsize=None, force_model=False,\n",
       "                is_fitted=False, model=None, percent=False, sample_weight=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(ConfusionMatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PrecisionRecallCurve(ap_score=True,\n",
       "                     ax=<matplotlib.axes._subplots.AxesSubplot object at 0x12dc96da0>,\n",
       "                     classes=None, encoder=LabelEncoder(), fill_area=True,\n",
       "                     fill_opacity=0.2, force_model=False, is_fitted=False,\n",
       "                     iso_f1_curves=False, iso_f1_values={0.2, 0.4, 0.6, 0.8},\n",
       "                     line_opacity=0.8, micro=True, model=None, per_class=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(PRCurve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ROCAUC(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x12dd24e10>,\n",
       "       classes=None, encoder=LabelEncoder(), force_model=False, is_fitted=False,\n",
       "       macro=True, micro=True, model=None, per_class=True)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(ROCAUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multiclass format is not supported\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DiscriminationThreshold(argmax='fscore',\n",
       "                        ax=<matplotlib.axes._subplots.AxesSubplot object at 0x12de8c828>,\n",
       "                        cv=0.1, exclude=None, fbeta=1.0, force_model=None,\n",
       "                        is_fitted=False, model=None, n_trials=50,\n",
       "                        quantiles=array([0.1, 0.5, 0.9]), random_state=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(DiscriminationThreshold, score=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
